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Clinical coding is critical for hospital reimbursement, quality assessment, and health care planning. In Scandinavia, however, coding is often done by junior doctors or medical secretaries, leading to high rates of coding errors. Artificial intelligence (AI) tools, particularly semiautomatic computer-assisted coding tools, have the potential to reduce the excessive burden of administrative and clinical documentation. To date, much of what we know regarding these tools comes from lab-based evaluations, which often fail to account for real-world complexity and variability in clinical text. This study aims to investigate whether an AI tool developed by by Norwegian Centre for E-health Research at the University Hospital of North Norway, Easy-ICD (International Classification of Diseases), can enhance clinical coding practices by reducing coding time and improving data quality in a realistic setting. We specifically examined whether improvements differ between long and short clinical notes, defined by word count. An AI tool, Easy-ICD, was developed to assist clinical coders and was tested for improving both accuracy and time in a 1:1 crossover randomized controlled trial conducted in Sweden and Norway. Participants were randomly assigned to 2 groups (Sequence AB or BA), and crossed over between coding longer texts (Period 1; mean 307, SD 90; words) versus shorter texts (Period 2; mean 166, SD 55; words), while using our tool versus not using our tool. This was a purely web-based trial, where participants were recruited through email. Coding time and accuracy were logged and analyzed using Mann-Whitney U tests for each of the 2 periods independently, due to differing text lengths in each period. The trial had 17 participants enrolled, but only data from 15 participants (300 coded notes) were analyzed, excluding 2 incomplete records. Based on the Mann-Whitney U test, the median coding time difference for longer clinical text sequences was 123 seconds (P<.001, 95% CI 81-164), representing a 46% reduction in median coding time when our tool was used. For shorter clinical notes, the median time difference of 11 seconds was not significant (P=.25, 95% CI -34 to 8). Coding accuracy improved with Easy-ICD for both longer (62% vs 67%) and shorter clinical notes (60% vs 70%), but these differences were not statistically significant (P=.50and P=.17, respectively). User satisfaction ratings (submitted for 37% of cases) showed slightly higher approval for the tool's suggestions on longer clinical notes. This study demonstrates the potential of AI to transform common tasks in clinical workflows, with ostensible positive impacts on work efficiencies for clinical coding tasks with more demanding longer text sequences. Further studies within hospital workflows are required before these presumed impacts can be more clearly understood.

Background This study assessed the accuracy of three International Classification of Diseases (ICD) codes methods derived from Global Burden of Disease (GBD) sepsis study (modified GBD method) in identifying sepsis, compared to the Angus method. Sources of errors in these methods were also reported. Methods Prospective multicentre, observational, study. Emergency Department patients aged ≥ 16 years with high sepsis risk from nine hospitals in NSW, Australia were screened for clinical sepsis using Sepsis 3 criteria and coded as having sepsis or not using the modified GBD and Angus methods. The three modified GBD methods were: Explicit —sepsis-specific ICD code recorded; Implicit —sepsis-specific code or infection as primary ICD code plus organ dysfunction code; Implicit plus —as for Implicit but infection as primary or secondary ICD code. Agreement between clinical sepsis and ICD coding methods was assessed using Cronbach alpha (α). For false positive cases (ICD-coded sepsis but not clinically diagnosed), the ICD codes leading to those errors were documented. For false negatives (clinically diagnosed sepsis but ICD-coded), uncoded sources of infection and organ dysfunction were documented. Results Of 6869 screened patients, 450 (median age 72.4 years, 48.9% females) met inclusion criteria. Clinical sepsis was diagnosed in 215/450 (47.8%). The explicit, implicit, implicit plus and Angus methods identified sepsis in 108/450 (24.0%), 175/450 (38.9%), 222/450 (49.3%) and 170/450 (37.8%), respectively. Sensitivity was 41.4%, 58.1%, 67.4% and 55.8%, and specificity 91.9%, 78.7%, 67.2% and 79.1%, respectively. Agreement between clinical sepsis and all ICD coding methods was low (α = 0.52–0.56). False positives were 19, 50, and 77, while false negatives were 126, 90, and 70 for the explicit, implicit, and implicit plus methods, respectively. For false positive cases, unspecified urinary tract infection, hypotension and acute kidney failure were commonly assigned infection and organ dysfunction codes. About half (44.3%-55.6%) of the false negative cases didn’t have a pathogen documented. Conclusion The modified GBD method demonstrated low accuracy in identifying sepsis; with the implicit plus method being the most accurate. Errors in identifying sepsis using ICD codes arise mostly from coding for unspecified urinary infections and associated organ dysfunction. Trial registration The study was registered at the ANZCTR (ACTRN12621000333819) on 24 March 2021.

The International Classification of Diseases, 11th Revision (ICD-11) has significantly improved the ability to navigate coding challenges beyond prior iterations of the ICD. Commonly encountered sources of complexity in clinical documentation include coding of uncertain and “ruled out” diagnoses. Assessing official international guidelines and rules, this paper documents extensive variation across countries in existing practices for coding and reporting unconfirmed and “ruled out” clinical concepts in ICD-10 (and modifications thereof). The design of ICD-11 is intended to mitigate these coding challenges by introducing postcoordination, expanding the range of codable clinical concepts, and offering clearer guidance in the ICD-11 Reference Guide. ICD-11 offers substantial progress towards more precise capture of uncertain and “ruled out” diagnoses, including international consensus on coding rules for these historically challenging clinical concepts. However, we identify the need for further clarification of the concepts of “provisional diagnosis” and “differential diagnosis.”

The need for efficient video coding technology is more important than ever in the current scenario where video applications are increasing worldwide, and Internet of Things (IoT) devices are becoming widespread. In this context, it is necessary to carefully review the recently completed MPEG-5 Essential Video Coding (EVC) standard because the EVC Baseline profile is customized to meet the specific requirements needed to process IoT video data in terms of low complexity. Nevertheless, the EVC Baseline profile has a notable disadvantage. Since it is a codec composed only of simple tools developed over 20 years, it tends to represent numerous coding artifacts. In particular, the presence of blocking artifacts at the block boundary is regarded as a critical issue that must be addressed. To address this, this paper proposes a post-filter using a block partitioning information-based Convolutional Neural Network (CNN). The proposed method in the experimental results objectively shows an approximately 0.57 dB for All-Intra (AI) and 0.37 dB for Low-Delay (LD) improvements in each configuration by the proposed method when compared to the pre-post-filter video, and the enhanced PSNR results in an overall bitrate reduction of 11.62% for AI and 10.91% for LD in the Luma and Chroma components, respectively. Due to the huge improvement in the PSNR, the proposed method significantly improved the visual quality subjectively, particularly in blocking artifacts at the coding block boundary.

Background The Saudi health transformation program entails a comprehensive reform of all health system functions. One of the pillars of this reform is the health care financing transformation. The Council of Health Insurance (CHI) aims to bring more transparency and understanding of case-mix through the introduction of patient classification and data standardization. Until recently, the private health insurance sector was using a variety of in-house non-standardized billing codes that impeded transparency and a value-based health care (VBHC) financing model. This study enabled the introduction of standardized billing codes known as the Saudi Billing System (SBS). Methods We reviewed and assessed several patient classification and billing systems as part of the assessment phase, followed by data collection from the three largest health insurance companies relating to eighty health care providers. A representative sample of 36,299 patient records were re-coded. Coding was undertaken using the Australian Classification of Health Interventions (ACHI) 10th Edition. Codes were assigned based on assessment by clinical coders using an established methodology and followed by an audit to confirm the assigned code or assign an alternative code where the coding could not be adequately completed by the initial coder. Results Seventy-five percent of records were mapped to an existing ACHI code, leaving 25% being a partial match, an approximate match or other (1%, 22% and 2% respectively). As part of this process, the original ACHI codes were modified, and additional codes were added, ensuring full compatibility with billing practices. We named the new code set the Saudi Billing System (SBS). As a result of this work, we created an additional 1,774 codes, bringing the total SBS code set to 7,947 codes (30% increase from ACHI 10th Edition). Conclusions Patient classification and standardized billing systems are critical for transparency in providing health care and financing. Working within the existing national patient classification mandate and clinical coding standards required innovative ways to adapt these systems to a private health insurance market (specificity, familiarity, existing license with modification rights and ability to build fee schedule), to address the requirements of a reformed and more value-based insurance market. Current mandated patient classification systems are a good basis for adaptation to serve the needs of the overall health care transformation in the country and a building block towards more transparency and VBHC.

The proportion of video traffic on the internet is expected to reach 82% by 2022, mainly due to the increasing number of consumers and the emergence of new video formats with more demanding features (depth, resolution, multiview, 360, etc.). Efforts are therefore being made to constantly improve video compression standards to minimize the necessary bandwidth while retaining high video quality levels. In this context, the Joint Collaborative Team on Video Coding has been analyzing new video coding technologies to improve the compression efficiency with respect to the HEVC video coding standard. A software package known as the Joint Exploration Test Model has been proposed to implement and evaluate new video coding tools. In this work, we present parallel versions of the JEM encoder that are particularly suited for shared memory platforms, and can significantly reduce its huge computational complexity. The proposed parallel algorithms are shown to achieve high levels of parallel efficiency. In particular, in the All Intra coding mode, the best of our proposed parallel versions achieves an average efficiency value of 93.4%. They also had high levels of scalability, as shown by the inclusion of an automatic load balancing mechanism.

String Prediction (SP) is a very efficient screen content coding (SCC) tool. In SP, the self-referencing string plays an important role to improve coding efficiency. But general self-referencing string has the problem of very low pixel copying throughput and is prohibited in the non-self-referencing based SP which has been adopted in the third-generation Audio Video Standard (AVS3). To overcome the problem and bring back the coding gain of self-referencing string, a line-based self-referencing string (LSRS) enabled SP technique is proposed. Moreover, to keep the pixel copying throughput and coding complexity of LSRS enabled SP the same as non-self-referencing based SP, an unbroken-line decomposition algorithm is presented to decompose an LSRS into multiple non-self-referencing strings. In this way, LSRS can be treated in the same way as a non-self-referencing string with the best trade-off between coding efficiency and complexity. Compared with non-self-referencing based SP, using AVS3 reference software HPM, for twelve SCC common test condition YUV test sequences in text and graphics with motion category and mixed content category, the proposed LSRS technique achieves the average Y BD-rate reduction of 0.81% and 0.59% as well as the maximum Y BD-rate reduction of 2.04% and 1.31% for All Intra and Low Delay configurations, respectively, with almost no additional encoding and decoding complexity. The proposed LSRS enabled SP technique has been adopted in AVS3.

The newest video compression standard, Versatile Video Coding (VVC), was finalized in July 2020 by the Joint Video Experts Team (JVET). Its main goal is to reduce the bitrate by 50% over its predecessor video coding standard, the High Efficiency Video Coding (HEVC). Due to the new advanced tools and features included in VVC, it actually provides high coding performances—for instance, the Quad Tree with nested Multi-Type Tree (QTMTT) involved in the partitioning block. Furthermore, VVC introduces various techniques that allow for superior performance compared to HEVC, but with an increase in the computational complexity. To tackle this complexity, a fast Coding Unit partition algorithm based on machine learning for the intra configuration in VVC is proposed in this work. The proposed algorithm is formed by five binary Light Gradient Boosting Machine (LightGBM) classifiers, which can directly predict the most probable split mode for each coding unit without passing through the exhaustive process known as Rate Distortion Optimization (RDO). These LightGBM classifiers were offline trained on a large dataset; then, they were embedded on the optimized implementation of VVC known as VVenC. The results of our experiment show that our proposed approach has good trade-offs in terms of time-saving and coding efficiency. Depending on the preset chosen, our approach achieves an average time savings of 30.21% to 82.46% compared to the VVenC encoder anchor, and a Bjøntegaard Delta Bitrate (BDBR) increase of 0.67% to 3.01%, respectively.

This paper proposes a flexible and uniform string matching technique named universal string matching (USM) for general screen content coding (SCC). USM uses two reference buffers for string matching: primary reference buffer (PRB) and secondary reference buffer (SRB), and includes three modes: general string (GS) mode, constrained string 1 (CS1) mode, and constrained string 2 (CS2) mode. PRB is used in GS mode and CS1 mode and SRB is used in GS mode and CS2 mode. Each of the three modes plays an essential role in SCC due to the diversity and comprehensiveness of the screen content. The experiments use HEVC SCC common test condition (CTC) for lossy coding. Compared with HEVC HM-16.6 + SCM-5.2 reference software of full frame search range for IBC and with ACT off, USM achieves an average Y BD-rate of −25.5% for four TGM (text and graphics with motion) test sequences from the SCC verification test suite and −5.5% for eight TGM test sequences from the HEVC SCC CTC test suite in all intra configuration with a small increase of encoding runtime and a small decrease of decoding runtime.

The audiovisual entertainment industry has entered a race to find the video encoder offering the best Rate/Distortion (R/D) performance for high-quality high-definition video content. The challenge consists in providing a moderate to low computational/hardware complexity encoder able to run Ultra High-Definition (UHD) video formats of different flavours (360°, AR/VR, etc.) with state-of-the-art R/D performance results. It is necessary to evaluate not only R/D performance, a highly important feature, but also the complexity of future video encoders. New coding tools offering a small increase in R/D performance at the cost of greater complexity are being advanced with caution. We performed a detailed analysis of two evolutions of High Efficiency Video Coding (HEVC) video standards, Joint Exploration Model (JEM) and Versatile Video Coding (VVC), in terms of both R/D performance and complexity. The results show how VVC, which represents the new direction of future standards, has, for the time being, sacrificed R/D performance in order to significantly reduce overall coding/decoding complexity.